In recent years, there has been a proliferation of low cost and high quality digital capture devices such as digital cameras, personal digital assistants (PDAs) and camera phones. Most of these devices are capable of recording both still and motion digital images and many of these devices incorporate wireless network access, touch screen interfaces, low cost internal, removable, and network accessible image storage, and various sensors to monitor the motion, orientation, and location of the device. These factors have enhanced the opportunities for “picture taking” and reduced the effort and expense of photography to the point where users are now amassing vast collections of digital images. As a result of this reduction of the cost, effort, and convenience thresholds to capturing digital images, accessing important images within these collections has become increasing difficult due to the sheer volume of content.
Various schemes have been developed for organizing collections of digital images and identifying important images within such collections, many of which rely on user actions to designate important images through means such as: tagging individual images as “favorites”, “star rating systems”, or providing “meaningful comments” which are linked to the images. These systems cannot identify important images unless the user makes the effort to provide this information.
Another approach for identifying important images relies on automated image analysis algorithms to rank images based on aesthetic or technical image quality. Aesthetic image quality relates to the attractiveness of an image to a human observer. Technical image quality ranking algorithms rate images according to quantitative metrics such as colorfulness, exposure, sharpness and noise. Such automatic image quality evaluation methods may not necessarily reflect the importance of a particular image to a user. For example, the user may cherish an important image of a newborn, a pet, or a lost loved one, that may be poorly composed, monochromatic, underexposed, and slightly out of focus, which would be ranked low by aesthetic and technical quality ranking algorithms.
U.S. Pat. No. 6,535,636 to Savakis, entitled “Method for automatically detecting digital images that are undesirable for placing in albums” teaches automatically determining an overall image quality parameter by assessing various technical image quality attributes (e.g., sharpness, contrast, noise, and exposure).
U.S. Pat. No. 6,658,139 to Coolcingham et al., entitled “Method for assessing overall quality of digital images” teaches a method determining a numerical representation of user perceived overall image quality of a digital image. The method involves creating a digital reference image series with each digital reference image having a corresponding numerical representation of overall image quality. User inputs are collected while iteratively displaying a digital test image in comparison with the digital reference images. The user inputs are analyzed to infer a numerical representation of the overall image quality of the digital test image.
U.S. Pat. No. 6,940,545 to Ray, entitled “Face detecting camera and method” teaches automatically assessing aesthetic image quality based on whether detected faces are positioned in a location consistent with the “rule of thirds.”
Pre- and post-capture user interaction monitoring has also been used to determine important images. Such approaches are based on the monitoring of user behavior, changes to user expressions, or changes to user physiology while capturing, viewing, or utilizing images. These techniques often involve additional devices such cameras to monitor, record, and analyze facial expressions or eye gaze or dilation, or devices that monitor galvanic skin response (GSR), heart rate, breathing rate or the like. In other cases, user interaction with images is monitored and recorded within the capture device to monitor user interactions with the image capture device. For example, interaction with the zoom control, exposure button, exposure modes and settings can be monitored to determine the level of effort the user engaged in to capture the image. Similarly post capture interaction, such as image review with a capture device's integrated display screen or after the images have been transferred to a computer or printer, these interactions can be analyzed to determine via utilization models which images are important to users.
U.S. Pat. No. 7,620,270 to Matraszek et al., entitled “Method for creating and using affective information in a digital imaging system” discloses a retrieval procedure for stored digital images based a user's affective information. The affective information is obtained by a signal detecting means representing an emotional reaction of the user to one of the stored digital images. Digital images are categorized based on the affective information.
U.S. Pat. No. 7,742,083 to Fredlund et al., entitled “In-camera dud image management,” teaches automatically determining a value index from one or more of: user inputs to said camera during capture, usage of a particular image record following capture, semantic image content of an image record, and user reactions to the image record. Image records are classified into unacceptable image records having value indexes within a predetermined threshold and acceptable image records having respective said value indexes beyond the predetermined threshold.
What is required is a system to identify important images that does not require additional devices or sensors to monitor user behavior, is not limited to generic aesthetic or quality standards, and does not place any additional burdens on users.